haldensify (0.0.5)

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Highly Adaptive Lasso Conditional Density Estimation.


Conditional density estimation is a longstanding and challenging problem in statistical theory, and numerous proposals exist for optimally estimating such complex functions. Algorithms for nonparametric estimation of conditional densities based on a pooled hazard regression formulation and semiparametric estimation via conditional hazards modeling are implemented based on the highly adaptive lasso, a nonparametric regression function for efficient estimation with fast convergence under mild assumptions. The pooled hazards formulation implemented was first described by Daz and van der Laan (2011) .

Maintainer: Nima Hejazi
Author(s): Nima Hejazi [aut, cre, cph] (<https://orcid.org/0000-0002-7127-2789>), David Benkeser [aut] (<https://orcid.org/0000-0002-1019-8343>), Mark van der Laan [aut, ths] (<https://orcid.org/0000-0003-1432-5511>)

License: MIT + file LICENSE

Uses: assertthat, data.table, future.apply, ggplot2, hal9001, origami, Rdpack, testthat, knitr, dplyr, rmarkdown, future

Released 24 days ago.



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